We aim to see the patterns of infant mortality among maternal populations based on their tobacco and alcohol consumption patterns. Additionally, we also want to see how demographic factors such as race, age, income levels, educational levels and intent to have children can also contribute to infant mortality rates.
# Plot of question and responses for alcohol
# Create ggplot object
gg_plot <- cleaned_alc_2007 %>%
ggplot(aes(x = question, fill = response)) +
geom_bar(position = "dodge") +
labs(title = "Questions and Responses", x = "Questions", y = "Count") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 5, size = 2)) +
labs(
x = "Question",
y = "Response",
title = "Questions vs Response of Alcohol Consumption"
)
# Extract data directly from the original data frame
plot_data <- cleaned_alc_2007 %>%
group_by(question, response) %>%
summarize(count = n())
## `summarise()` has grouped output by 'question'. You can override using
## the `.groups` argument.
# Convert data to Plotly
plot_ly(data = plot_data, x = ~question, y = ~count, color = ~response, type = "bar", split = ~response) %>%
layout(
title = "Questions vs Response of Alcohol Consumption",
xaxis = list(title = "Question",tickfont = list(size = 5)),
yaxis = list(title = "Response"),
barmode = "stack"
)
<<<<<<< HEAD
=======
>>>>>>> 61ad8b81651148b582c777369aef8cb1b06c3d0b
Plot 1 shows the number of individuals who consumed alcohol, resumed drinking alcohol after quitting briefly, or reduced the total number of drinks consumed. The x-axis shows the indicators from the CDC 2007 PRAM data set, and y-axis shows the data values. Overall, it looks like the number of mothers who had the same drinks or more had the highest amount of data values.
library(ggplot2)
library(plotly)
library(dplyr)
# Create ggplot object
gg_plot <- cleaned_tobac_2007 %>%
ggplot(aes(x = location_abbr, fill = response)) +
geom_bar(position = "dodge") +
labs(title = "Questions and Responses", x = "Questions", y = "Count") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(
x = "Question",
y = "Response",
title = "Tobacco Use by State"
)
# Extract data directly from the original data frame
plot_data <- cleaned_tobac_2007 %>%
group_by(location_abbr, response) %>%
summarize(count = n())
<<<<<<< HEAD
## `summarise()` has grouped output by 'location_abbr'. You
## can override using the `.groups` argument.
=======
## `summarise()` has grouped output by 'location_abbr'. You can override
## using the `.groups` argument.
>>>>>>> 61ad8b81651148b582c777369aef8cb1b06c3d0b
# Convert data to Plotly
plot_ly(data = plot_data, x = ~location_abbr, y = ~count, color = ~response, type = "bar", split = ~response) %>%
layout(
title = " Tobacco Use by State",
xaxis = list(title = "Question"),
yaxis = list(title = "Response"),
barmode = "stack"
)
<<<<<<< HEAD
=======
>>>>>>> 61ad8b81651148b582c777369aef8cb1b06c3d0b
Plot 2 shows tobacco consumption by state according to the 2007 CDC PRAM data set. The highest data values were amongst those who answered “yes” and the lowest data values were among those who were specific and answered “41+/ day.”
leaflet() |>
addTiles() |>
addCircleMarkers(data = cleaned_alc_2007,
lng = ~longitude, # Adjust column name if needed
lat = ~latitude, # Adjust column name if needed
label = ~location_abbr, # Assuming 'Group.1' is a column in your data
radius = ~data_value * 0.12,
color = "blue",
stroke = TRUE,
fillOpacity = 0.1,
popup = ~paste("Response:", response))
<<<<<<< HEAD
=======
>>>>>>> 61ad8b81651148b582c777369aef8cb1b06c3d0b
This map visualizes data points from the
cleaned_tobac_2007 dataset, showcasing the prevalence of
Tobacco Use during pregnancy across different states. The sizes of the
circles represent the values in the data_value column,
reflecting the number of participants who responded affirmatively to
Tobacco Use during pregnancy in each state. Larger circles indicate a
higher number of positive responses, offering an overview of the
distribution of this behavior across geographic regions
leaflet() %>%
addTiles() %>%
addCircleMarkers(data = cleaned_tobac_2007,
lng = ~longitude,
lat = ~latitude,
label = ~location_abbr,
radius = ~data_value * 0.12,
color = "magenta",
stroke = TRUE,
fillOpacity = 0.1,
popup = ~paste("Response:", response),
group = ~location_abbr)
<<<<<<< HEAD
=======
>>>>>>> 61ad8b81651148b582c777369aef8cb1b06c3d0b
This map illustrates data points from the
cleaned_tobac_2007 dataset, depicting the prevalence of
Tobacco Use during pregnancy across various states. The sizes of the
circles correspond to the values in the data_value column,
reflecting the number of participants who reported affirmative responses
to Tobacco Use during pregnancy in each state. Larger circles indicate a
higher number of positive responses, providing insight into the regional
distribution of this behavior
leaflet() %>%
addTiles() %>%
addCircleMarkers(data = cleaned_infant_mortality,
lng = ~longitude,
lat = ~latitude,
label = ~location_abbr,
radius = ~data_value * 0.12,
color = "orange",
stroke = TRUE,
fillOpacity = 0.5,
popup = ~paste("Response:", response))
<<<<<<< HEAD
=======
>>>>>>> 61ad8b81651148b582c777369aef8cb1b06c3d0b
This Leaflet plot provides a visual representation of infant
mortality data on a map. The size of the circles corresponds to the
values in the data_value column, indicating the rate of
infant mortality for each location.
The plot above shows the locations of infant mortality rate across the US.
infant_deaths <- cleaned_infant_mortality %>%
filter(question == "Indicator of infant currently alive" & response == "NO") %>%
group_by(location_desc) %>%
summarize(total_infant_deaths = n())
# Display the table using knitr::kable()
knitr::kable(infant_deaths)
| location_desc | total_infant_deaths |
|---|---|
| Alaska | 45 |
| Arkansas | 45 |
| Colorado | 47 |
| Delaware | 40 |
| Georgia | 43 |
| Hawaii | 45 |
| Illinois | 47 |
| Maine | 42 |
| Maryland | 45 |
| Massachusetts | 44 |
| Michigan | 43 |
| Minnesota | 41 |
| Missouri | 42 |
| Nebraska | 45 |
| New Jersey | 39 |
| New York (excluding NYC) | 47 |
| New York City | 47 |
| North Carolina | 47 |
| Ohio | 46 |
| Oklahoma | 47 |
| Oregon | 46 |
| Pennsylvania | 3 |
| Rhode Island | 46 |
| South Carolina | 47 |
| South Dakota | 43 |
| Utah | 47 |
| Vermont | 47 |
| Washington | 43 |
| West Virginia | 47 |
| Wisconsin | 40 |
| Wyoming | 43 |
The table provides a summary of total infant deaths by state, with
each row representing a specific location. The
location_desc column denotes the state, and the
total_infant_deaths column indicates the corresponding
number of infant deaths in each location. The data suggests variability
in infant mortality rates across different regions, with some areas
reporting higher or lower rates than others. For instance, states like
Pennsylvania have a notably lower count of infant deaths, while others,
such as Alaska and Arkansas, have higher counts. However, most of the
data seemed to stay within the 35 to 50 range. This summary provides an
overview of the distribution of infant deaths across various
geographical locations.
filtered_mortality_race <- cleaned_infant_mortality %>%
filter(break_out_category == "Maternal Race/Ethnicity" &
(break_out %in% c("Hispanic", "Non-hispanic", "White, non-Hispanic")) &
question == "Indicator of infant currently alive" & response == "NO")
# Display the table using knitr::kable()
knitr::kable(filtered_mortality_race)
| year | location_abbr | location_desc | class | topic | question | data_source | response | data_value | low_confidence_limit | high_confidence_limit | sample_size | break_out | break_out_category | latitude | longitude | class_id | topic_id | question_id | location_id | break_out_id | break_out_categoryid | response_id |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2007 | UT | Utah | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.9 | 0.3 | 2.7 | 5 | Hispanic | Maternal Race/Ethnicity | 39.36070 | -111.58713 | CLA8 | TOP43 | QUO143 | 49 | ETH2 | BOC6 | RES23 |
| 2007 | OR | Oregon | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.7 | 0.2 | 2.1 | 3 | Hispanic | Maternal Race/Ethnicity | 44.56745 | -120.15503 | CLA8 | TOP43 | QUO143 | 41 | ETH2 | BOC6 | RES23 |
| 2007 | WA | Washington | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.2 | 0.0 | 1.7 | 1 | White, non-Hispanic | Maternal Race/Ethnicity | 47.52228 | -120.47001 | CLA8 | TOP43 | QUO143 | 53 | ETH4 | BOC6 | RES23 |
| 2007 | YC | New York City | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.4 | 0.2 | 0.7 | 10 | Hispanic | Maternal Race/Ethnicity | 42.82700 | -75.54397 | CLA8 | TOP43 | QUO143 | 36 | ETH2 | BOC6 | RES23 |
| 2007 | OH | Ohio | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.6 | 0.3 | 1.4 | 18 | White, non-Hispanic | Maternal Race/Ethnicity | 40.06021 | -82.40426 | CLA8 | TOP43 | QUO143 | 39 | ETH4 | BOC6 | RES23 |
| 2007 | ME | Maine | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | NA | NA | NA | NA | Hispanic | Maternal Race/Ethnicity | 45.25423 | -68.98503 | CLA8 | TOP43 | QUO143 | 23 | ETH2 | BOC6 | RES23 |
| 2007 | MD | Maryland | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.6 | 0.3 | 1.3 | 7 | Hispanic | Maternal Race/Ethnicity | 39.29058 | -76.60926 | CLA8 | TOP43 | QUO143 | 24 | ETH2 | BOC6 | RES23 |
| 2007 | ME | Maine | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.3 | 0.2 | 0.4 | 19 | White, non-Hispanic | Maternal Race/Ethnicity | 45.25423 | -68.98503 | CLA8 | TOP43 | QUO143 | 23 | ETH4 | BOC6 | RES23 |
| 2007 | MA | Massachusetts | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.6 | 0.1 | 2.2 | 2 | Hispanic | Maternal Race/Ethnicity | 42.27687 | -72.08269 | CLA8 | TOP43 | QUO143 | 25 | ETH2 | BOC6 | RES23 |
| 2007 | IL | Illinois | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.4 | 0.2 | 0.6 | 12 | White, non-Hispanic | Maternal Race/Ethnicity | 40.48501 | -88.99771 | CLA8 | TOP43 | QUO143 | 17 | ETH4 | BOC6 | RES23 |
| 2007 | DE | Delaware | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 1.8 | 0.6 | 5.4 | 4 | Hispanic | Maternal Race/Ethnicity | 39.00883 | -75.57774 | CLA8 | TOP43 | QUO143 | 10 | ETH2 | BOC6 | RES23 |
| 2007 | MO | Missouri | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.8 | 0.5 | 1.5 | 18 | White, non-Hispanic | Maternal Race/Ethnicity | 38.63579 | -92.56630 | CLA8 | TOP43 | QUO143 | 29 | ETH4 | BOC6 | RES23 |
| 2007 | AR | Arkansas | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.3 | 0.1 | 1.0 | 2 | Hispanic | Maternal Race/Ethnicity | 34.74865 | -92.27449 | CLA8 | TOP43 | QUO143 | 5 | ETH2 | BOC6 | RES23 |
| 2007 | RI | Rhode Island | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.3 | 0.2 | 0.6 | 5 | Hispanic | Maternal Race/Ethnicity | 41.70828 | -71.52247 | CLA8 | TOP43 | QUO143 | 44 | ETH2 | BOC6 | RES23 |
| 2007 | WV | West Virginia | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | NA | NA | NA | NA | Hispanic | Maternal Race/Ethnicity | 38.66551 | -80.71264 | CLA8 | TOP43 | QUO143 | 54 | ETH2 | BOC6 | RES23 |
| 2007 | UT | Utah | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.5 | 0.2 | 1.0 | 15 | White, non-Hispanic | Maternal Race/Ethnicity | 39.36070 | -111.58713 | CLA8 | TOP43 | QUO143 | 49 | ETH4 | BOC6 | RES23 |
| 2007 | MA | Massachusetts | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.5 | 0.1 | 1.9 | 2 | White, non-Hispanic | Maternal Race/Ethnicity | 42.27687 | -72.08269 | CLA8 | TOP43 | QUO143 | 25 | ETH4 | BOC6 | RES23 |
| 2007 | AR | Arkansas | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.5 | 0.2 | 1.1 | 18 | White, non-Hispanic | Maternal Race/Ethnicity | 34.74865 | -92.27449 | CLA8 | TOP43 | QUO143 | 5 | ETH4 | BOC6 | RES23 |
| 2007 | SD | South Dakota | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | NA | NA | NA | NA | Hispanic | Maternal Race/Ethnicity | 44.35313 | -100.37353 | CLA8 | TOP43 | QUO143 | 46 | ETH2 | BOC6 | RES23 |
| 2007 | HI | Hawaii | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.4 | 0.1 | 2.5 | 1 | White, non-Hispanic | Maternal Race/Ethnicity | 21.30485 | -157.85775 | CLA8 | TOP43 | QUO143 | 15 | ETH4 | BOC6 | RES23 |
| 2007 | VT | Vermont | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | NA | NA | NA | NA | Hispanic | Maternal Race/Ethnicity | 43.62538 | -72.51764 | CLA8 | TOP43 | QUO143 | 50 | ETH2 | BOC6 | RES23 |
| 2007 | MD | Maryland | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.1 | 0.1 | 0.3 | 8 | White, non-Hispanic | Maternal Race/Ethnicity | 39.29058 | -76.60926 | CLA8 | TOP43 | QUO143 | 24 | ETH4 | BOC6 | RES23 |
| 2007 | MN | Minnesota | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 1.1 | 0.2 | 7.4 | 1 | Hispanic | Maternal Race/Ethnicity | 46.35565 | -94.79420 | CLA8 | TOP43 | QUO143 | 27 | ETH2 | BOC6 | RES23 |
| 2007 | IL | Illinois | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.6 | 0.2 | 1.9 | 4 | Hispanic | Maternal Race/Ethnicity | 40.48501 | -88.99771 | CLA8 | TOP43 | QUO143 | 17 | ETH2 | BOC6 | RES23 |
| 2007 | WA | Washington | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.2 | 0.0 | 1.3 | 1 | Hispanic | Maternal Race/Ethnicity | 47.52228 | -120.47001 | CLA8 | TOP43 | QUO143 | 53 | ETH2 | BOC6 | RES23 |
| 2007 | NY | New York (excluding NYC) | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.4 | 0.1 | 1.0 | 4 | Hispanic | Maternal Race/Ethnicity | 42.82700 | -75.54397 | CLA8 | TOP43 | QUO143 | 36 | ETH2 | BOC6 | RES23 |
| 2007 | OR | Oregon | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.6 | 0.2 | 1.7 | 18 | White, non-Hispanic | Maternal Race/Ethnicity | 44.56745 | -120.15503 | CLA8 | TOP43 | QUO143 | 41 | ETH4 | BOC6 | RES23 |
| 2007 | NC | North Carolina | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.7 | 0.3 | 1.3 | 9 | Hispanic | Maternal Race/Ethnicity | 35.46622 | -79.15925 | CLA8 | TOP43 | QUO143 | 37 | ETH2 | BOC6 | RES23 |
| 2007 | NJ | New Jersey | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.2 | 0.1 | 0.7 | 3 | White, non-Hispanic | Maternal Race/Ethnicity | 40.13057 | -74.27369 | CLA8 | TOP43 | QUO143 | 34 | ETH4 | BOC6 | RES23 |
| 2007 | NY | New York (excluding NYC) | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.6 | 0.3 | 1.4 | 19 | White, non-Hispanic | Maternal Race/Ethnicity | 42.82700 | -75.54397 | CLA8 | TOP43 | QUO143 | 36 | ETH4 | BOC6 | RES23 |
| 2007 | MI | Michigan | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.5 | 0.3 | 1.1 | 12 | White, non-Hispanic | Maternal Race/Ethnicity | 44.66132 | -84.71439 | CLA8 | TOP43 | QUO143 | 26 | ETH4 | BOC6 | RES23 |
| 2007 | NE | Nebraska | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.4 | 0.1 | 2.3 | 1 | Hispanic | Maternal Race/Ethnicity | 41.64104 | -99.36572 | CLA8 | TOP43 | QUO143 | 31 | ETH2 | BOC6 | RES23 |
| 2007 | CO | Colorado | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.6 | 0.3 | 1.1 | 24 | White, non-Hispanic | Maternal Race/Ethnicity | 38.84384 | -106.13361 | CLA8 | TOP43 | QUO143 | 8 | ETH4 | BOC6 | RES23 |
| 2007 | YC | New York City | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.6 | 0.2 | 1.7 | 8 | White, non-Hispanic | Maternal Race/Ethnicity | 42.82700 | -75.54397 | CLA8 | TOP43 | QUO143 | 36 | ETH4 | BOC6 | RES23 |
| 2007 | NE | Nebraska | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.2 | 0.0 | 1.1 | 1 | White, non-Hispanic | Maternal Race/Ethnicity | 41.64104 | -99.36572 | CLA8 | TOP43 | QUO143 | 31 | ETH4 | BOC6 | RES23 |
| 2007 | WY | Wyoming | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.4 | 0.2 | 0.7 | 7 | White, non-Hispanic | Maternal Race/Ethnicity | 43.23554 | -108.10983 | CLA8 | TOP43 | QUO143 | 56 | ETH4 | BOC6 | RES23 |
| 2007 | DE | Delaware | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.2 | 0.1 | 0.7 | 2 | White, non-Hispanic | Maternal Race/Ethnicity | 39.00883 | -75.57774 | CLA8 | TOP43 | QUO143 | 10 | ETH4 | BOC6 | RES23 |
| 2007 | WI | Wisconsin | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.4 | 0.1 | 1.8 | 2 | White, non-Hispanic | Maternal Race/Ethnicity | 44.39319 | -89.81637 | CLA8 | TOP43 | QUO143 | 55 | ETH4 | BOC6 | RES23 |
| 2007 | CO | Colorado | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.4 | 0.2 | 0.9 | 11 | Hispanic | Maternal Race/Ethnicity | 38.84384 | -106.13361 | CLA8 | TOP43 | QUO143 | 8 | ETH2 | BOC6 | RES23 |
| 2007 | GA | Georgia | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.3 | 0.1 | 0.7 | 5 | White, non-Hispanic | Maternal Race/Ethnicity | 32.83968 | -83.62758 | CLA8 | TOP43 | QUO143 | 13 | ETH4 | BOC6 | RES23 |
| 2007 | AK | Alaska | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.6 | 0.2 | 1.6 | 12 | White, non-Hispanic | Maternal Race/Ethnicity | 64.84508 | -147.72206 | CLA8 | TOP43 | QUO143 | 2 | ETH4 | BOC6 | RES23 |
| 2007 | RI | Rhode Island | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.5 | 0.3 | 1.1 | 18 | White, non-Hispanic | Maternal Race/Ethnicity | 41.70828 | -71.52247 | CLA8 | TOP43 | QUO143 | 44 | ETH4 | BOC6 | RES23 |
| 2007 | NC | North Carolina | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.8 | 0.3 | 1.7 | 20 | White, non-Hispanic | Maternal Race/Ethnicity | 35.46622 | -79.15925 | CLA8 | TOP43 | QUO143 | 37 | ETH4 | BOC6 | RES23 |
| 2007 | MN | Minnesota | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.3 | 0.1 | 1.0 | 4 | White, non-Hispanic | Maternal Race/Ethnicity | 46.35565 | -94.79420 | CLA8 | TOP43 | QUO143 | 27 | ETH4 | BOC6 | RES23 |
| 2007 | SC | South Carolina | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.2 | 0.1 | 0.3 | 6 | Hispanic | Maternal Race/Ethnicity | 33.99882 | -81.04537 | CLA8 | TOP43 | QUO143 | 45 | ETH2 | BOC6 | RES23 |
| 2007 | SC | South Carolina | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.7 | 0.3 | 2.0 | 46 | White, non-Hispanic | Maternal Race/Ethnicity | 33.99882 | -81.04537 | CLA8 | TOP43 | QUO143 | 45 | ETH4 | BOC6 | RES23 |
| 2007 | OH | Ohio | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 5.7 | 0.8 | 31.2 | 1 | Hispanic | Maternal Race/Ethnicity | 40.06021 | -82.40426 | CLA8 | TOP43 | QUO143 | 39 | ETH2 | BOC6 | RES23 |
| 2007 | WV | West Virginia | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 1.0 | 0.5 | 1.9 | 31 | White, non-Hispanic | Maternal Race/Ethnicity | 38.66551 | -80.71264 | CLA8 | TOP43 | QUO143 | 54 | ETH4 | BOC6 | RES23 |
| 2007 | OK | Oklahoma | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.3 | 0.2 | 0.5 | 13 | Hispanic | Maternal Race/Ethnicity | 35.47203 | -97.52107 | CLA8 | TOP43 | QUO143 | 40 | ETH2 | BOC6 | RES23 |
| 2007 | HI | Hawaii | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.7 | 0.2 | 2.7 | 2 | Hispanic | Maternal Race/Ethnicity | 21.30485 | -157.85775 | CLA8 | TOP43 | QUO143 | 15 | ETH2 | BOC6 | RES23 |
| 2007 | VT | Vermont | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.6 | 0.3 | 1.2 | 11 | White, non-Hispanic | Maternal Race/Ethnicity | 43.62538 | -72.51764 | CLA8 | TOP43 | QUO143 | 50 | ETH4 | BOC6 | RES23 |
| 2007 | OK | Oklahoma | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.4 | 0.3 | 0.5 | 73 | White, non-Hispanic | Maternal Race/Ethnicity | 35.47203 | -97.52107 | CLA8 | TOP43 | QUO143 | 40 | ETH4 | BOC6 | RES23 |
| 2007 | NJ | New Jersey | Infant Health | Pregnancy Outcome | Indicator of infant currently alive | PRAMS | NO | 0.9 | 0.3 | 2.7 | 3 | Hispanic | Maternal Race/Ethnicity | 40.13057 | -74.27369 | CLA8 | TOP43 | QUO143 | 34 | ETH2 | BOC6 | RES23 |
view(filtered_mortality_race)
This table shows in depth information about infant mortality rate by race-ethnicity in the 31 states in this sample. The race/ethnicity variable was re-categorized into just “Hispanic” and “White-non Hispanic” to give us our variables of interest. Using this information, we will visualize the relationship between infant mortality rate and how race/ethnicity plays a role.
plot_infant_deaths <- ggplot(filtered_mortality_race, aes(x = break_out, fill = break_out)) +
geom_bar() +
labs(title = "Infant Deaths by Ethnicity",
x = "Ethnicity",
y = "Total Infant Deaths") +
scale_fill_manual(values = c("Hispanic" = "blue", "Non-hispanic" = "green", "White, non-Hispanic" = "pink")) +
theme_minimal()
print(plot_infant_deaths)

The plot_infant_deaths above shows a plot of infant
deaths categorized by whether they were Hispanic or not. The graph shows
that those who were not Hispanic had a higher infant death count than
those who were Hispanic.
filtered_mortality_income <- cleaned_infant_mortality %>%
filter(break_out_category == "Income (years 2004 and beyond)" &
(break_out %in% c("Less than $10,000", "$10,000 to $24,999", "$25,000 to $49,999", "$50,000 or more")) &
question == "Indicator of infant currently alive" & response == "NO")
view(filtered_mortality_income)
plot_infant_income <- ggplot(filtered_mortality_income, aes(x = break_out, fill = break_out)) +
geom_bar() +
labs(title = "Infant Deaths by Income",
x = "Maternal Income",
y = "Total Infant Deaths") +
scale_fill_manual(values = c("Less than $10,000" = "blue", "$10,000 to $24,999" = "red", "$25,000 to $49,999" = "purple", "$50,000 or more" = "pink")) +
theme_minimal()
print(plot_infant_income)

This plot shows the relationship between maternal income levels and infant mortality rate. From the bar graph, those in the income bracket of $25,000-$49,000 had a higher infant mortality rate and those who made more than $50,000 had the lowest. This was an interesting observation, as those who made less that $10,000 a year would be expected to have the highest infant mortality as low income levels is a social determinant of infant health outcomes.
filtered_mortality_age <- cleaned_infant_mortality %>%
filter(break_out_category == "Maternal Age - 18 to 44 years in groupings" &
(break_out %in% c("Age < 18", "Age 18 - 24", "Age 25 - 29", "Age 30 - 44", "Age 45+")) &
question == "Indicator of infant currently alive" & response == "NO")
plot_mortality_age <- ggplot(filtered_mortality_age, aes(x = break_out, fill = break_out)) +
geom_bar() +
labs(title = "Infant Deaths by Maternal Age",
x = "Maternal Age",
y = "Total Infant Deaths") +
scale_fill_manual(values = c("Age < 18" = "blue", "Age 18 - 24" = "purple", "Age 25 - 29" = "pink", "Age 30 - 44" = "yellow", "Age 45+" = "orange")) +
theme_minimal()
print(plot_mortality_age)

This plot shows infant mortality rates by age. Interestingly, maternal populations below the age of 18 have the lowest infant mortality rates. Comparatively, women in the 45+ age category have a higher infant mortality rate. Women in the 18-24, 25-29 and 30-44 all have approximately the same levels of infant mortality rates.
filtered_mortality_educ <- cleaned_infant_mortality %>%
filter(break_out_category == "Maternal Education" &
(break_out %in% c("<12 yrs", "12 yrs", ">12 yrs")) &
question == "Indicator of infant currently alive" & response == "NO")
plot_mortality_educ <- filtered_mortality_educ |>
ggplot(aes(x = break_out, fill = break_out)) +
geom_bar() +
labs(title = "Infant Deaths by Maternal Education",
x = "Maternal Education",
y = "Total Infant Deaths") +
scale_fill_manual(values = c("<12 yrs" = "blue", "12 yrs" = "purple", ">12 yrs" = "pink")) +
theme_minimal()
print(plot_mortality_educ)

This plot shows the relationship between maternal education and infant mortality rates. This is an interesting finding that shows those with greater than 12 years of education had higher infant mortality rates compared to those with less than 12 years of education. This could be because factors like race could have been a confounding variable. Additionally, it can also be assumed that people who were in school longer were also older, so that could also be an additional factor.
filtered_mortality_medi <- cleaned_infant_mortality %>%
filter(break_out_category == "Medicaid Recipient" &
(break_out %in% c("Non-Medicaid", "Medicaid")) &
question == "Indicator of infant currently alive" & response == "NO")
plot_mortality_medi <- filtered_mortality_medi |>
ggplot(aes(x = break_out, fill = break_out)) +
geom_bar() +
labs(title = "Infant Deaths by Medicaid Recpient",
x = "Medicaid Recepient",
y = "Total Infant Deaths") +
scale_fill_manual(values = c("Non-Medicaid" = "blue", "Medicaid" = "purple")) +
theme_minimal()
print(plot_mortality_medi)

This plot shows the relationship between infant mortality rate and mothers who were recipients of medicaid. This bar graph shows that there is no statistical difference.
filtered_mortality_intent <- cleaned_infant_mortality %>%
filter(break_out_category == "Pregnancy Intendedness" &
(break_out %in% c("Unintended", "Intended")) &
question == "Indicator of infant currently alive" & response == "NO")
plot_mortality_intent <- filtered_mortality_intent |>
ggplot(aes(x = break_out, fill = break_out)) +
geom_bar() +
labs(title = "Infant Deaths by Maternal Intent",
x = "Maternal Intent",
y = "Total Infant Deaths") +
scale_fill_manual(values = c("Unintended" = "orange", "Intended" = "red")) +
theme_minimal()
print(plot_mortality_intent)

This plot shows the relationship between maternal intent to have children with infant mortality rate. Those who intended to have a child, actually had a higher rate of infant mortality rate. There could be several confounding factors that could contribute to this, such as age and stress factors.